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in both fundamental and applied research topics related to the modelling of thermal-fluid systems, with applications in aeronautics (unsteady aerodynamics, flow control), aerospace (atmospheric re
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for age-related macular degeneration (AMD). Computational modelling enables researchers to integrate several types of biological data into a single framework, to test perturbations in silico, and to predict
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conditions e.g. temperature, humidity, carbonation; to investigate macro-scale and micro-scale models for prediction (AI tools will be used as appropriate) to infer correlation and for physics-informed
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Pullman, Washington, specializing in economic entomology and ecological pest management. The successful candidate will use digital agriculture, predictive modeling, and research on resilience to weather
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doctoral candidate on the MSCA-DN DETECTIVE project. For rapid control of virus outbreaks, fast availability of antivirals is a major bottleneck. In DETECTIVE, innovative models will be used for setting up
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precipitation, leading to scaling within reservoirs and production wells that reduces operational efficiency. Predicting where these processes will occur is a challenge because they are controlled in part by
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integrated AI methods for the analysis, design, monitoring, and control of floating offshore systems. The PhD project focuses on the experimental characterisation and computational modelling of smart materials
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Danfoss, you will combine thermofluid modelling, reduced-order multiphysics methods, and nonlinear rotor dynamics analysis to develop predictive modelling tools that support the industrial design of
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planning at the control and planning layer. You will develop scalable trajectory optimization and real-time Model Predictive Control (MPC) strategies that use the swarm’s belief state to ensure safety
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disturbances. Current control methods generally rely on simplified interaction models based on constant aerodynamic coefficients, quasi-static approximations, potential flow models, or experimentally identified